from keras.datasets import mnist
from keras import models
from keras import layers
from keras.utils import to_categorical
import matplotlib.pyplot as plt


model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
print(model.summary())

# (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
#
# train_images = train_images.reshape((60000, 28, 28, 1))
# train_images = train_images.astype('float32') / 255
#
# test_images = test_images.reshape((10000, 28, 28, 1))
# test_images = test_images.astype('float32') / 255
#
# train_labels = to_categorical(train_labels)
# test_labels = to_categorical(test_labels)
#
# model.compile(optimizer='rmsprop',
#               loss='categorical_crossentropy',
#               metrics=['accuracy'])
# model.fit(train_images, train_labels, epochs=5, batch_size=64)
